{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-11-07T11:59:43.768681600Z",
     "start_time": "2024-11-07T11:59:43.760382300Z"
    }
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   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "from PIL import Image, ImageDraw\n",
    "\n",
    "# 配置\n",
    "model_predict_data = r\"F:\\Data\\预处理benchmark\\pictogram_benchmark\\output\"\n",
    "generate_dataset_images_path = r\"F:\\Data\\benchmark\\data\\images\"\n",
    "generate_dataset_labels_path = r\"F:\\Data\\benchmark\\data\\labels.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "################################# 预测折线掩码可视化 ##############################\n",
    "model_predict = os.path.join(model_predict_data, \"Lineformer.json\")\n",
    "####################################################################################\n",
    "with open(model_predict, \"r\") as fp:\n",
    "    lineformer_data = json.load(fp)\n",
    "\n",
    "\n",
    "colors = [\n",
    "    (255, 255, 0),\n",
    "    (255, 0, 255),\n",
    "    (0, 255, 255),\n",
    "    (120, 255, 0),\n",
    "    (120, 0, 255),\n",
    "    (0, 120, 255),\n",
    "    (120, 120, 0),\n",
    "    (0 ,0, 0),\n",
    "    (255, 0, 0),\n",
    "    (0, 255, 0),\n",
    "    (0, 0, 255)\n",
    "]\n",
    "\n",
    "\n",
    "for filename in lineformer_data.keys():\n",
    "    filepath = os.path.join(generate_dataset_images_path, filename)\n",
    "    img = Image.open(filepath)\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    lines = lineformer_data[filename]\n",
    "    \n",
    "    cnt = 0\n",
    "    for line in lines:\n",
    "        for point in line:\n",
    "            draw.rectangle((point[0] - 2, point[1] - 2, point[0] + 2, point[1] + 2), fill=colors[cnt])    \n",
    "        cnt += 1\n",
    "        cnt %= len(colors)\n",
    "    # gt and pred\n",
    "    # new_image = Image.new('RGB', (img.width + img.width, img.height))\n",
    "    # o_img = Image.open(filepath)\n",
    "    # # 读取标注\n",
    "    # new_image.paste(o_img, (0, 0))\n",
    "    # new_image.paste(img, (img.width, 0))\n",
    "    \n",
    "    # only pred\n",
    "    new_image = Image.new('RGB', (img.width, img.height))\n",
    "    new_image.paste(img, (0, 0))\n",
    "    \n",
    "    output_path = os.path.join(\"./images/line\", filename)\n",
    "    new_image.save(output_path)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-11-07T11:59:47.250916900Z",
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    }
   },
   "id": "4f96e1ddea77fc8a"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "################################# 预测条形图数据元素可视化 预定义 ##############################\n",
    "model_predict = os.path.join(model_predict_data, \"BarDetect.json\")\n",
    "#########################################################################################\n",
    "\n",
    "with open(model_predict, \"r\") as fp:\n",
    "    bardetect_data = json.load(fp)\n",
    "\n",
    "\n",
    "colors = [\n",
    "    (0 ,0, 0),\n",
    "    (255, 255, 0),\n",
    "    (255, 0, 255),\n",
    "    (0, 255, 255),\n",
    "    (120, 255, 0),\n",
    "    (120, 0, 255),\n",
    "    (0, 120, 255),\n",
    "    (120, 120, 0),\n",
    "    (255, 0, 0),\n",
    "    (0, 255, 0),\n",
    "    (0, 0, 255)\n",
    "]\n",
    "\n",
    "\n",
    "for filename in bardetect_data.keys():\n",
    "    filepath = os.path.join(generate_dataset_images_path, filename)\n",
    "    img = Image.open(filepath)\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    rectangles = bardetect_data[filename][\"data\"]\n",
    "    \n",
    "    cnt = 0\n",
    "    for rectangle_group in rectangles:\n",
    "        for item in rectangle_group:\n",
    "            rectangle = item[0]\n",
    "            x1,y1,x2,y2 = rectangle\n",
    "            draw.rectangle((int(x1), int(y1), int(x2), int(y2)), outline=colors[cnt], width=3)\n",
    "    # new_image = Image.new('RGB', (img.width + img.width, img.height))\n",
    "    # o_img = Image.open(filepath)\n",
    "    # new_image.paste(o_img, (0, 0))\n",
    "    # new_image.paste(img, (img.width, 0))\n",
    "    # only pred\n",
    "    new_image = Image.new('RGB', (img.width, img.height))\n",
    "    new_image.paste(img, (0, 0))\n",
    "    \n",
    "    output_path = os.path.join(\"./images/bar\", filename)\n",
    "    new_image.save(output_path)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-11-07T11:59:56.718675100Z",
     "start_time": "2024-11-07T11:59:55.051774100Z"
    }
   },
   "id": "eaa8b44b58d83fb2"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "################################# 预测饼图数据元素可视化 预定义 ##############################\n",
    "model_predict = os.path.join(model_predict_data, \"PieDetect.json\")\n",
    "#########################################################################################\n",
    "\n",
    "with open(model_predict, \"r\") as fp:\n",
    "    bardetect_data = json.load(fp)\n",
    "\n",
    "colors = [\n",
    "    (0 ,0, 0),\n",
    "    (255, 255, 0),\n",
    "    (255, 0, 255),\n",
    "    (0, 255, 255),\n",
    "    (120, 255, 0),\n",
    "    (120, 0, 255),\n",
    "    (0, 120, 255),\n",
    "    (120, 120, 0),\n",
    "    (255, 0, 0),\n",
    "    (0, 255, 0),\n",
    "    (0, 0, 255)\n",
    "]\n",
    "\n",
    "\n",
    "for filename in bardetect_data.keys():\n",
    "    filepath = os.path.join(generate_dataset_images_path, filename)\n",
    "    img = Image.open(filepath)\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    pies = bardetect_data[filename][\"data\"]\n",
    "    \n",
    "    cnt = 0\n",
    "    for pie in pies:\n",
    "        cx,cy = pie[2]\n",
    "        # print(pie[-1][0])\n",
    "        x1,y1,x2,y2 = pie[-1]\n",
    "        # print(x1, y1, cx, cy, x2, y2)\n",
    "        draw.polygon([(x1+cx, y1+cy), (cx, cy), (x2+cx, y2+cy)], outline=colors[cnt], width=3)\n",
    "        cnt += 1 \n",
    "    # new_image = Image.new('RGB', (img.width + img.width, img.height))\n",
    "    # o_img = Image.open(filepath)\n",
    "    # new_image.paste(o_img, (0, 0))\n",
    "    #new_image.paste(img, (img.width, 0))\n",
    "    \n",
    "    # only pred\n",
    "    new_image = Image.new('RGB', (img.width, img.height))\n",
    "    new_image.paste(img, (0, 0))\n",
    "    \n",
    "    output_path = os.path.join(\"./images/pie\", filename)\n",
    "    new_image.save(output_path)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-11-07T11:59:59.915970900Z",
     "start_time": "2024-11-07T11:59:59.009831200Z"
    }
   },
   "id": "51c6155d20a6891e"
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  {
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   "execution_count": 12,
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     "name": "stdout",
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      "036_00006_2.png\n",
      "BarCanvas\n",
      "Title\n",
      "Legend\n",
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      "PieCanvas\n",
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      "BarCanvas\n",
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      "BarCanvas\n",
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      "PieCanvas\n",
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      "BarCanvas\n",
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      "036_00011_1.png\n",
      "BarCanvas\n",
      "Legend\n",
      "Title\n"
     ]
    }
   ],
   "source": [
    "from PIL import ImageFont\n",
    "\n",
    "################################# 预测公共元素可视化 预定义 ##############################\n",
    "yolo_model_predict = os.path.join(model_predict_data, \"YoloCls.json\")\n",
    "corner_model_predict = os.path.join(model_predict_data, \"CornerCls.json\")\n",
    "#########################################################################################\n",
    "\n",
    "class ComponentDefinition:\n",
    "    Legend = 0\n",
    "    ValueTitle = 1\n",
    "    ChartTitle = 2\n",
    "    CategoryTitle = 3\n",
    "    # 数据元素绘图区域 plot_area\n",
    "    Canvas = 5\n",
    "\n",
    "\n",
    "def combine_cls_predict(yolocls_results, cornercls_results):\n",
    "    combine_results = dict()\n",
    "\n",
    "    for filename in yolocls_results.keys():\n",
    "        yolocls_result = yolocls_results[filename]\n",
    "        cornercls_result = cornercls_results[filename]\n",
    "        yolocls_data = yolocls_result[\"data\"]\n",
    "        cornercls_data = cornercls_result[\"data\"]\n",
    "\n",
    "        chart_type = yolocls_data[0]\n",
    "        corner_cls_info = cornercls_data[1]\n",
    "        yolo_cls_info = yolocls_data[1]\n",
    "\n",
    "        combine_result = dict()\n",
    "\n",
    "        combine_result[\"data_type\"] = chart_type\n",
    "\n",
    "        if \"5\" in yolo_cls_info.keys():\n",
    "            combine_result[ComponentDefinition.Canvas - 1 + chart_type] = yolo_cls_info[\"5\"]\n",
    "        else:\n",
    "            if \"5\" in corner_cls_info.keys():\n",
    "                combine_result[ComponentDefinition.Canvas - 1 + chart_type] = corner_cls_info[\"5\"]\n",
    "\n",
    "        # 将corner net的其他检测结果写入\n",
    "        for key in corner_cls_info.keys():\n",
    "            if int(key) < 4:\n",
    "                combine_result[int(key)] = corner_cls_info[key]\n",
    "\n",
    "        # yolo检测器其他公共元素的检测结果作为补充\n",
    "        extra_component_detected_by_yolo = [key for key in yolo_cls_info.keys() if\n",
    "                                            int(key) < 4 and key not in corner_cls_info.keys()]\n",
    "        for key in extra_component_detected_by_yolo:\n",
    "            combine_result[int(key)] = yolo_cls_info[key]\n",
    "\n",
    "        combine_results[filename] = combine_result\n",
    "\n",
    "    return combine_results\n",
    "        \n",
    "\n",
    "LABELS = {\n",
    "    \"Legend\": 0,\n",
    "    \"ValueTitle\": 1,\n",
    "    \"Title\": 2,\n",
    "    \"CategoryTitle\": 3,\n",
    "    \"BarCanvas\": 4,\n",
    "    \"LineCanvas\": 5,\n",
    "    \"PieCanvas\": 6,\n",
    "    \"Bar\": 7,\n",
    "    \"Line\": 8,\n",
    "    \"Pie\": 9,\n",
    "    \"Pictogram\": 10\n",
    "}\n",
    "\n",
    "R_LABELS = {}\n",
    "for key in LABELS.keys():\n",
    "    value = LABELS[key]\n",
    "    R_LABELS[value] = key\n",
    "\n",
    "with open(yolo_model_predict, \"r\") as fp:\n",
    "    yolo_model_predict_data = json.load(fp)\n",
    "with open(corner_model_predict, \"r\") as fp:\n",
    "    corner_model_predict_data = json.load(fp)\n",
    "with open(generate_dataset_labels_path, \"r\") as fp:\n",
    "    gt_labels_data = json.load(fp)\n",
    "\n",
    "combine_data = combine_cls_predict(yolo_model_predict_data, corner_model_predict_data)\n",
    "\n",
    "colors = [\n",
    "    (255, 255, 0),\n",
    "    (255, 0, 255),\n",
    "    (0, 255, 255),\n",
    "    (120, 255, 0),\n",
    "    (120, 0, 255),\n",
    "    (0, 120, 255),\n",
    "    (120, 120, 0),\n",
    "    (255, 0, 0),\n",
    "    (0, 255, 0),\n",
    "    (0, 0, 255)\n",
    "]\n",
    "\n",
    "\n",
    "for filename in combine_data.keys():\n",
    "    filepath = os.path.join(generate_dataset_images_path, filename)\n",
    "    img = Image.open(filepath)\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    o_img = Image.open(filepath)\n",
    "    o_draw = ImageDraw.Draw(o_img)\n",
    "    \n",
    "    cls = combine_data[filename]\n",
    "    \n",
    "    true_filename = filename.split(\"\\\\\")[-1]\n",
    "    gt = gt_labels_data[true_filename]\n",
    "\n",
    "    cnt = 0\n",
    "    font_path = \"arial.ttf\"\n",
    "    font_size = 24  # 设置字体大小\n",
    "    font = ImageFont.truetype(font_path, font_size)\n",
    "    \n",
    "    print(filename)\n",
    "    detected_label_text = []\n",
    "    for key in cls.keys():\n",
    "        if not isinstance(key, int):\n",
    "            continue\n",
    "        value = cls[key]\n",
    "        label_text = R_LABELS[key]\n",
    "        detected_label_text.append(label_text)\n",
    "        font_area_w = len(label_text) * font_size * 0.6\n",
    "        draw.rectangle((value[0], value[1], value[2], value[3]), outline=colors[cnt], width=3)\n",
    "        draw.rectangle((value[0], value[1] - font_size, value[0] + font_area_w, value[1]), fill=colors[cnt])\n",
    "        draw.text((value[0], value[1] - font_size), label_text, fill=(0,0,0), font=font)\n",
    "        \n",
    "        # print(label_text, gt.keys())\n",
    "        if label_text in gt.keys():\n",
    "            bbox = gt[label_text]\n",
    "            print(label_text)\n",
    "            o_draw.rectangle((bbox[0], bbox[1], bbox[2], bbox[3]), outline=colors[cnt], width=3) \n",
    "            o_draw.rectangle((bbox[0], bbox[1] - font_size, bbox[0] + font_area_w, bbox[1]), fill=colors[cnt])\n",
    "            o_draw.text((bbox[0], bbox[1] - font_size), label_text, fill=(0,0,0), font=font)\n",
    "        \n",
    "        cnt += 1\n",
    "        cnt %= len(colors)\n",
    "    \n",
    "    for label_text in gt.keys():\n",
    "        if label_text in detected_label_text or label_text not in LABELS.keys() or LABELS[label_text] > 6:\n",
    "            continue\n",
    "        detected_label_text.append(label_text)\n",
    "        font_area_w = len(label_text) * font_size * 0.6\n",
    "        bbox = gt[label_text]\n",
    "        o_draw.rectangle((bbox[0], bbox[1], bbox[2], bbox[3]), outline=colors[cnt], width=3) \n",
    "        o_draw.rectangle((bbox[0], bbox[1] - font_size, bbox[0] + font_area_w, bbox[1]), fill=colors[cnt])\n",
    "        o_draw.text((bbox[0], bbox[1] - font_size), label_text, fill=(0,0,0), font=font)\n",
    "    \n",
    "    \n",
    "    # new_image = Image.new('RGB', (img.width + img.width, img.height))\n",
    "    # new_image.paste(o_img, (0, 0))\n",
    "    # new_image.paste(img, (img.width, 0))\n",
    "    # only pred\n",
    "    new_image = Image.new('RGB', (img.width, img.height))\n",
    "    new_image.paste(img, (0, 0))\n",
    "    \n",
    "    output_path = os.path.join(\"./images/cls\", filename)\n",
    "    new_image.save(output_path)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-11-07T12:06:09.681648500Z",
     "start_time": "2024-11-07T12:06:02.060522900Z"
    }
   },
   "id": "cd871751e2ec9f2a"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# CACHED\n",
    "from PIL import ImageFont\n",
    "\n",
    "################################# 预测公共元素可视化 预定义 ##############################\n",
    "cached_model_predict_path = os.path.join(model_predict_data, \"CachedCls.json\")\n",
    "#########################################################################################\n",
    "        \n",
    "LABELS = {\n",
    "    \"Legend\": 0,\n",
    "    \"ValueTitle\": 1,\n",
    "    \"Title\": 2,\n",
    "    \"CategoryTitle\": 3,\n",
    "    \"BarCanvas\": 4,\n",
    "    \"LineCanvas\": 5,\n",
    "    \"PieCanvas\": 6,\n",
    "    \"Bar\": 7,\n",
    "    \"Line\": 8,\n",
    "    \"Pie\": 9,\n",
    "    \"Pictogram\": 10\n",
    "}\n",
    "\n",
    "R_LABELS = {}\n",
    "for key in LABELS.keys():\n",
    "    value = LABELS[key]\n",
    "    R_LABELS[value] = key\n",
    "\n",
    "with open(generate_dataset_labels_path, \"r\") as fp:\n",
    "    gt_labels_data = json.load(fp)\n",
    "with open(cached_model_predict_path, \"r\") as fp:\n",
    "    cached_model_predict_data = json.load(fp)\n",
    "\n",
    "colors = [\n",
    "    (255, 255, 0),\n",
    "    (255, 0, 255),\n",
    "    (0, 255, 255),\n",
    "    (120, 255, 0),\n",
    "    (120, 0, 255),\n",
    "    (0, 120, 255),\n",
    "    (120, 120, 0),\n",
    "    (255, 0, 0),\n",
    "    (0, 255, 0),\n",
    "    (0, 0, 255)\n",
    "]\n",
    "\n",
    "\n",
    "for filename in cached_model_predict_data.keys():\n",
    "    filepath = os.path.join(generate_dataset_images_path, filename)\n",
    "    img = Image.open(filepath)\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    o_img = Image.open(filepath)\n",
    "    \n",
    "    cls = cached_model_predict_data[filename]\n",
    "    \n",
    "    filename = filename.split(\"\\\\\")[-1]\n",
    "\n",
    "    cnt = 0\n",
    "    font_path = \"arial.ttf\"\n",
    "    font_size = 24  # 设置字体大小\n",
    "    font = ImageFont.truetype(font_path, font_size)\n",
    "    \n",
    "    detected_label_text = []\n",
    "    for key in cls.keys():\n",
    "        value = cls[key]\n",
    "        label_text = key\n",
    "        detected_label_text.append(label_text)\n",
    "        font_area_w = len(label_text) * font_size * 0.6\n",
    "        for item in value:\n",
    "            bbox = item[0]\n",
    "            draw.rectangle((bbox[0], bbox[1], bbox[2], bbox[3]), outline=colors[cnt], width=3)\n",
    "            draw.rectangle((bbox[0], bbox[1] - font_size, bbox[0] + font_area_w, bbox[1]), fill=colors[cnt])\n",
    "            draw.text((bbox[0], bbox[1] - font_size), label_text, fill=(0,0,0), font=font)\n",
    "        \n",
    "        cnt += 1\n",
    "        cnt %= len(colors)\n",
    "    \n",
    "    # gt和pred\n",
    "    # new_image = Image.new('RGB', (img.width + img.width, img.height))\n",
    "    # new_image.paste(o_img, (0, 0))\n",
    "    # new_image.paste(img, (img.width, 0))\n",
    "    # only pred\n",
    "    new_image = Image.new('RGB', (img.width, img.height))\n",
    "    new_image.paste(img, (0, 0))\n",
    "    \n",
    "    \n",
    "    output_path = os.path.join(\"./images/cached\", filename)\n",
    "    new_image.save(output_path)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-11-07T12:01:00.187908300Z",
     "start_time": "2024-11-07T12:00:53.375861400Z"
    }
   },
   "id": "ec1efd20a6a308b3"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6e329e33a63973e3"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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